140 lines
3.7 KiB
Python
140 lines
3.7 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sys import exit
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import wquantiles as wq
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import numpy as np
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from functools import partial
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import sys
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def convert64(x):
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return np.int64(int(x, base=16))
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def convert8(x):
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return np.int8(int(x, base=16))
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df = pd.read_csv(sys.argv[1],
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dtype={
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"main_core": np.int8,
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"helper_core": np.int8,
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# "address": int,
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# "hash": np.int8,
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"time": np.int16,
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"clflush_remote_hit": np.int32,
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"clflush_shared_hit": np.int32,
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"clflush_miss_f": np.int32,
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"clflush_local_hit_f": np.int32,
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"clflush_miss_n": np.int32,
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"clflush_local_hit_n": np.int32,
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"reload_miss": np.int32,
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"reload_remote_hit": np.int32,
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"reload_shared_hit": np.int32,
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"reload_local_hit": np.int32},
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converters={'address': convert64, 'hash': convert8},
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)
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sample_columns = [
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"clflush_remote_hit",
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"clflush_shared_hit",
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"clflush_miss_f",
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"clflush_local_hit_f",
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"clflush_miss_n",
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"clflush_local_hit_n",
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"reload_miss",
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"reload_remote_hit",
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"reload_shared_hit",
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"reload_local_hit",
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]
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sample_flush_columns = [
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"clflush_remote_hit",
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"clflush_shared_hit",
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"clflush_miss_f",
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"clflush_local_hit_f",
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"clflush_miss_n",
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"clflush_local_hit_n",
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]
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print(df.columns)
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#df["Hash"] = df["Addr"].apply(lambda x: (x >> 15)&0x3)
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print(df.head())
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print(df["hash"].unique())
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min_time = df["time"].min()
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max_time = df["time"].max()
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q10s = [wq.quantile(df["time"], df[col], 0.1) for col in sample_flush_columns]
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q90s = [wq.quantile(df["time"], df[col], 0.9) for col in sample_flush_columns]
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graph_upper = int(((max(q90s) + 19) // 10) * 10)
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graph_lower = int(((min(q10s) - 10) // 10) * 10)
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# graph_lower = (min_time // 10) * 10
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# graph_upper = ((max_time + 9) // 10) * 10
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print("graphing between {}, {}".format(graph_lower, graph_upper))
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df_main_core_0 = df[df["main_core"] == 0]
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#df_helper_core_0 = df[df["helper_core"] == 0]
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g = sns.FacetGrid(df_main_core_0, col="helper_core", row="hash", legend_out=True)
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g2 = sns.FacetGrid(df, col="main_core", row="hash", legend_out=True)
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colours = ["b", "r", "g", "y"]
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def custom_hist(x, *y, **kwargs):
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for (i, yi) in enumerate(y):
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kwargs["color"] = colours[i]
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sns.distplot(x, range(graph_lower, graph_upper), hist_kws={"weights": yi, "histtype":"step"}, kde=False, **kwargs)
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# Color convention here :
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# Blue = miss
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# Red = Remote Hit
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# Green = Local Hit
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# Yellow = Shared Hit
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g.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")
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g2.map(custom_hist, "time", "clflush_miss_n", "clflush_remote_hit", "clflush_local_hit_n", "clflush_shared_hit")
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# g.map(sns.distplot, "time", hist_kws={"weights": df["clflush_hit"]}, kde=False)
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plt.show()
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#plt.figure()
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exit(0)
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def stat(x, key):
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return wq.median(x["Time"], x[key])
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miss = df.groupby(["Core", "Hash"]).apply(stat, "ClflushMiss")
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stats = miss.reset_index()
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stats.columns = ["Core", "Hash", "Miss"]
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hit = df.groupby(["Core", "Hash"]).apply(stat, "ClflushHit")
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stats["Hit"] = hit.values
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print(stats.to_string())
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g = sns.FacetGrid(stats, row="Core")
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g.map(sns.distplot, 'Miss', bins=range(100, 480), color="r")
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g.map(sns.distplot, 'Hit', bins=range(100, 480))
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plt.show()
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#stats["clflush_miss_med"] = stats[[0]].apply(lambda x: x["miss_med"])
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#stats["clflush_hit_med"] = stats[[0]].apply(lambda x: x["hit_med"])
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#del df[[0]]
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#print(hit.to_string(), miss.to_string())
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# test = pd.DataFrame({"value" : [0, 5], "weight": [5, 1]})
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# plt.figure()
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# sns.distplot(test["value"], hist_kws={"weights": test["weight"]}, kde=False)
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exit(0)
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